Małgorzata J. Zimoń
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View article: Concerning the Use of Turbulent Flow Data for Machine Learning
Concerning the Use of Turbulent Flow Data for Machine Learning Open
This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the pitf…
View article: Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation
Reducing data resolution for better super-resolution: Reconstructing turbulent flows from noisy observation Open
A super-resolution (SR) method for the reconstruction of Navier-Stokes (NS) flows from noisy observations is presented. In the SR method, first the observation data is averaged over a coarse grid to reduce the noise at the expense of losin…
View article: Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery
Spectrally Decomposed Diffusion Models for Generative Turbulence Recovery Open
We investigate the statistical recovery of missing physics and turbulent phenomena in fluid flows using generative machine learning. Here we develop a two-stage super-resolution method using spectral filtering to restore the high-wavenumbe…
View article: Molecular rotations trigger a glass-to-plastic fcc heterogeneous crystallization in high-pressure water
Molecular rotations trigger a glass-to-plastic fcc heterogeneous crystallization in high-pressure water Open
We report a molecular dynamics study of the heterogeneous crystallization of high-pressure glassy water using (plastic) ice VII as a substrate. We focus on the thermodynamic conditions P ∈ [6–8] GPa and T ∈ [100–500] K, at which (plastic) …
View article: Efficient Adaptive Stochastic Collocation Strategies for Advection-Diffusion Problems with Uncertain Inputs
Efficient Adaptive Stochastic Collocation Strategies for Advection-Diffusion Problems with Uncertain Inputs Open
Physical models with uncertain inputs are commonly represented as parametric partial differential equations (PDEs). That is, PDEs with inputs that are expressed as functions of parameters with an associated probability distribution. Develo…
View article: Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks
Super Resolution for Turbulent Flows in 2D: Stabilized Physics Informed Neural Networks Open
We propose a new design of a neural network for solving a zero shot super resolution problem for turbulent flows. We embed Luenberger-type observer into the network's architecture to inform the network of the physics of the process, and to…
View article: Thermal transients in a U-bend
Thermal transients in a U-bend Open
We study numerically the propagation of a hot thermal transient through a\nU-bend via an ensemble of wall-resolved large eddy simulations. Conjugate heat\ntransfer between fluid and solid domains is accounted for. The flow is in a\nfully t…
View article: Uncertainty Quantification at the Molecular–Continuum Model Interface
Uncertainty Quantification at the Molecular–Continuum Model Interface Open
Non-equilibrium molecular dynamics simulations are widely employed to study transport fluid properties. Observables measured at the atomistic level can serve as inputs for continuum calculations, allowing for improved analysis of phenomena…
View article: An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications
An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications Open
Filtering of particle-based simulation data can lead to reduced computational costs and enable more efficient information transfer in multi-scale modelling. This paper compares the effectiveness of various signal processing methods to redu…
View article: A novel coupling of noise reduction algorithms for particle flow simulations
A novel coupling of noise reduction algorithms for particle flow simulations Open